RTS-GAT: Spatial Graph Attention-Based Spatio-Temporal Flow Prediction for Big Data Retailing

Abstract

Intelligent logistics is a crucial element in the era of innovative retailing. The retail industry may benefit from more sophisticated logistics forecasting, management, and collaboration to lower costs, boost efficiency, and improve service standards. It is vital to intelligent retail’s optimization of logistics and retail product transportation. Decoupling the retail road network traffic flow big data from the temporal or spatial dimension and creating strong correlations are the keys to effective modeling since it is a high-dimensional, Spatio-temporal sequence. While the spatial dimension does not correlate with the temporal dimension of the retail road network traffic flow, the temporal dimension varies continuously. This feature allows us to separate the spatial-temporal sequence of retail road network traffic flow into spatial graph data and model it primarily using the spatial correlation paradigm. In this study, we examine the variables that affect the retail road network’s traffic flow based on the demand for human travel. And we suggest a unique deep learning-based representation and fusion method of temporal, spatial, and traffic flow features as features of the spatial graph nodes of the shared spatial graph neural network framework TSGAT. Individual primitive features are restricted to be simultaneously learned and optimized in the feature space associated with each other in the modeling context of shared model parameters, and efficient eigenfeature representations are learned. After rigorous and comprehensive experimental validation, the TSGAT model achieves the best performance to date on multiple datasets.